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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_extraction.text</span></code>.TfidfTransformer</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-feature-extraction-text-tfidftransformer">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.feature_extraction.text.TfidfTransformer</span></code></a></li>
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  <div class="section" id="sklearn-feature-extraction-text-tfidftransformer">
<h1><a class="reference internal" href="../classes.html#module-sklearn.feature_extraction.text" title="sklearn.feature_extraction.text"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_extraction.text</span></code></a>.TfidfTransformer<a class="headerlink" href="#sklearn-feature-extraction-text-tfidftransformer" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.feature_extraction.text.TfidfTransformer">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.feature_extraction.text.</code><code class="sig-name descname">TfidfTransformer</code><span class="sig-paren">(</span><em class="sig-param">norm='l2'</em>, <em class="sig-param">use_idf=True</em>, <em class="sig-param">smooth_idf=True</em>, <em class="sig-param">sublinear_tf=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/feature_extraction/text.py#L1329"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform a count matrix to a normalized tf or tf-idf representation</p>
<p>Tf means term-frequency while tf-idf means term-frequency times inverse
document-frequency. This is a common term weighting scheme in information
retrieval, that has also found good use in document classification.</p>
<p>The goal of using tf-idf instead of the raw frequencies of occurrence of a
token in a given document is to scale down the impact of tokens that occur
very frequently in a given corpus and that are hence empirically less
informative than features that occur in a small fraction of the training
corpus.</p>
<p>The formula that is used to compute the tf-idf for a term t of a document d
in a document set is tf-idf(t, d) = tf(t, d) * idf(t), and the idf is
computed as idf(t) = log [ n / df(t) ] + 1 (if <code class="docutils literal notranslate"><span class="pre">smooth_idf=False</span></code>), where
n is the total number of documents in the document set and df(t) is the
document frequency of t; the document frequency is the number of documents
in the document set that contain the term t. The effect of adding “1” to
the idf in the equation above is that terms with zero idf, i.e., terms
that occur in all documents in a training set, will not be entirely
ignored.
(Note that the idf formula above differs from the standard textbook
notation that defines the idf as
idf(t) = log [ n / (df(t) + 1) ]).</p>
<p>If <code class="docutils literal notranslate"><span class="pre">smooth_idf=True</span></code> (the default), the constant “1” is added to the
numerator and denominator of the idf as if an extra document was seen
containing every term in the collection exactly once, which prevents
zero divisions: idf(d, t) = log [ (1 + n) / (1 + df(d, t)) ] + 1.</p>
<p>Furthermore, the formulas used to compute tf and idf depend
on parameter settings that correspond to the SMART notation used in IR
as follows:</p>
<p>Tf is “n” (natural) by default, “l” (logarithmic) when
<code class="docutils literal notranslate"><span class="pre">sublinear_tf=True</span></code>.
Idf is “t” when use_idf is given, “n” (none) otherwise.
Normalization is “c” (cosine) when <code class="docutils literal notranslate"><span class="pre">norm='l2'</span></code>, “n” (none)
when <code class="docutils literal notranslate"><span class="pre">norm=None</span></code>.</p>
<p>Read more in the <a class="reference internal" href="../feature_extraction.html#text-feature-extraction"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>norm</strong><span class="classifier">‘l1’, ‘l2’ or None, optional (default=’l2’)</span></dt><dd><p>Each output row will have unit norm, either:
* ‘l2’: Sum of squares of vector elements is 1. The cosine
similarity between two vectors is their dot product when l2 norm has
been applied.
* ‘l1’: Sum of absolute values of vector elements is 1.
See <code class="xref py py-func docutils literal notranslate"><span class="pre">preprocessing.normalize</span></code></p>
</dd>
<dt><strong>use_idf</strong><span class="classifier">boolean (default=True)</span></dt><dd><p>Enable inverse-document-frequency reweighting.</p>
</dd>
<dt><strong>smooth_idf</strong><span class="classifier">boolean (default=True)</span></dt><dd><p>Smooth idf weights by adding one to document frequencies, as if an
extra document was seen containing every term in the collection
exactly once. Prevents zero divisions.</p>
</dd>
<dt><strong>sublinear_tf</strong><span class="classifier">boolean (default=False)</span></dt><dd><p>Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>idf_</strong><span class="classifier">array, shape (n_features)</span></dt><dd><p>The inverse document frequency (IDF) vector; only defined
if  <code class="docutils literal notranslate"><span class="pre">use_idf</span></code> is True.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="r1b90ac3ca370-yates2011"><span class="brackets">R1b90ac3ca370-Yates2011</span></dt>
<dd><p>R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern
Information Retrieval. Addison Wesley, pp. 68-74.</p>
</dd>
<dt class="label" id="r1b90ac3ca370-mrs2008"><span class="brackets">R1b90ac3ca370-MRS2008</span></dt>
<dd><p>C.D. Manning, P. Raghavan and H. Schütze  (2008).
Introduction to Information Retrieval. Cambridge University
Press, pp. 118-120.</p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">TfidfTransformer</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">CountVectorizer</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">corpus</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;this is the first document&#39;</span><span class="p">,</span>
<span class="gp">... </span>          <span class="s1">&#39;this document is the second document&#39;</span><span class="p">,</span>
<span class="gp">... </span>          <span class="s1">&#39;and this is the third one&#39;</span><span class="p">,</span>
<span class="gp">... </span>          <span class="s1">&#39;is this the first document&#39;</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">vocabulary</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;this&#39;</span><span class="p">,</span> <span class="s1">&#39;document&#39;</span><span class="p">,</span> <span class="s1">&#39;first&#39;</span><span class="p">,</span> <span class="s1">&#39;is&#39;</span><span class="p">,</span> <span class="s1">&#39;second&#39;</span><span class="p">,</span> <span class="s1">&#39;the&#39;</span><span class="p">,</span>
<span class="gp">... </span>              <span class="s1">&#39;and&#39;</span><span class="p">,</span> <span class="s1">&#39;one&#39;</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">pipe</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([(</span><span class="s1">&#39;count&#39;</span><span class="p">,</span> <span class="n">CountVectorizer</span><span class="p">(</span><span class="n">vocabulary</span><span class="o">=</span><span class="n">vocabulary</span><span class="p">)),</span>
<span class="gp">... </span>                 <span class="p">(</span><span class="s1">&#39;tfid&#39;</span><span class="p">,</span> <span class="n">TfidfTransformer</span><span class="p">())])</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">corpus</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">pipe</span><span class="p">[</span><span class="s1">&#39;count&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">corpus</span><span class="p">)</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
<span class="go">array([[1, 1, 1, 1, 0, 1, 0, 0],</span>
<span class="go">       [1, 2, 0, 1, 1, 1, 0, 0],</span>
<span class="go">       [1, 0, 0, 1, 0, 1, 1, 1],</span>
<span class="go">       [1, 1, 1, 1, 0, 1, 0, 0]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">pipe</span><span class="p">[</span><span class="s1">&#39;tfid&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">idf_</span>
<span class="go">array([1.        , 1.22314355, 1.51082562, 1.        , 1.91629073,</span>
<span class="go">       1.        , 1.91629073, 1.91629073])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">pipe</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">corpus</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(4, 8)</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.feature_extraction.text.TfidfTransformer.fit" title="sklearn.feature_extraction.text.TfidfTransformer.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X[, y])</p></td>
<td><p>Learn the idf vector (global term weights)</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.feature_extraction.text.TfidfTransformer.fit_transform" title="sklearn.feature_extraction.text.TfidfTransformer.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(self, X[, y])</p></td>
<td><p>Fit to data, then transform it.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.feature_extraction.text.TfidfTransformer.get_params" title="sklearn.feature_extraction.text.TfidfTransformer.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.feature_extraction.text.TfidfTransformer.set_params" title="sklearn.feature_extraction.text.TfidfTransformer.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.feature_extraction.text.TfidfTransformer.transform" title="sklearn.feature_extraction.text.TfidfTransformer.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(self, X[, copy])</p></td>
<td><p>Transform a count matrix to a tf or tf-idf representation</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.feature_extraction.text.TfidfTransformer.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">norm='l2'</em>, <em class="sig-param">use_idf=True</em>, <em class="sig-param">smooth_idf=True</em>, <em class="sig-param">sublinear_tf=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/feature_extraction/text.py#L1435"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_extraction.text.TfidfTransformer.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/feature_extraction/text.py#L1442"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Learn the idf vector (global term weights)</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">sparse matrix, [n_samples, n_features]</span></dt><dd><p>a matrix of term/token counts</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_extraction.text.TfidfTransformer.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">**fit_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L544"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">numpy array of shape [n_samples, n_features]</span></dt><dd><p>Training set.</p>
</dd>
<dt><strong>y</strong><span class="classifier">numpy array of shape [n_samples]</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Additional fit parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">numpy array of shape [n_samples, n_features_new]</span></dt><dd><p>Transformed array.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_extraction.text.TfidfTransformer.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_extraction.text.TfidfTransformer.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_extraction.text.TfidfTransformer.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">copy=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/feature_extraction/text.py#L1474"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_extraction.text.TfidfTransformer.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform a count matrix to a tf or tf-idf representation</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">sparse matrix, [n_samples, n_features]</span></dt><dd><p>a matrix of term/token counts</p>
</dd>
<dt><strong>copy</strong><span class="classifier">boolean, default True</span></dt><dd><p>Whether to copy X and operate on the copy or perform in-place
operations.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>vectors</strong><span class="classifier">sparse matrix, [n_samples, n_features]</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-feature-extraction-text-tfidftransformer">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.feature_extraction.text.TfidfTransformer</span></code><a class="headerlink" href="#examples-using-sklearn-feature-extraction-text-tfidftransformer" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="The dataset used in this example is the 20 newsgroups dataset which will be automatically downl..."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_grid_search_text_feature_extraction_thumb.png" src="../../_images/sphx_glr_grid_search_text_feature_extraction_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/model_selection/grid_search_text_feature_extraction.html#sphx-glr-auto-examples-model-selection-grid-search-text-feature-extraction-py"><span class="std std-ref">Sample pipeline for text feature extraction and evaluation</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This is an example showing how the scikit-learn can be used to cluster documents by topics usin..."><div class="figure align-default" id="id4">
<img alt="../../_images/sphx_glr_plot_document_clustering_thumb.png" src="../../_images/sphx_glr_plot_document_clustering_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/text/plot_document_clustering.html#sphx-glr-auto-examples-text-plot-document-clustering-py"><span class="std std-ref">Clustering text documents using k-means</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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